Breakdown of category-specific word representations in a brain-constrained neurocomputational model of semantic dementia

The neurobiological nature of semantic knowledge, i.e., the encoding and storage of conceptual information in the human brain, remains a poorly understood and hotly debated subject. Clinical data on semantic deficits and neuroimaging evidence from healthy individuals have suggested multiple cortical regions to be involved in the processing of meaning. These include semantic hubs (most notably, anterior temporal lobe, ATL) that take part in semantic processing in general as well as sensorimotor areas that process specific aspects/categories according to their modality. Biologically inspired neurocomputational models can help elucidate the exact roles of these regions in the functioning of the semantic system and, importantly, in its breakdown in neurological deficits. We used a neuroanatomically constrained computational model of frontotemporal cortices implicated in word acquisition and processing, and adapted it to simulate and explain the effects of semantic dementia (SD) on word processing abilities. SD is a devastating, yet insufficiently understood progressive neurodegenerative disease, characterised by semantic knowledge deterioration that is hypothesised to be specifically related to neural damage in the ATL. The behaviour of our brain-based model is in full accordance with clinical data—namely, word comprehension performance decreases as SD lesions in ATL progress, whereas word repetition abilities remain less affected. Furthermore, our model makes predictions about lesion- and category-specific effects of SD: our simulation results indicate that word processing should be more impaired for object- than for action-related words, and that degradation of white matter should produce more severe consequences than the same proportion of grey matter decay. In sum, the present results provide a neuromechanistic explanatory account of cortical-level language impairments observed during the onset and progress of semantic dementia.


Microstructure
Each area consists of two neuronal layers, each of 625 (25x25) cells, one containing excitatory cells and one containing inhibitory ones (in what follows, referred to as eand i-cells, respectively).To avoid any potential edge effects, layers have a toroidal structure: the top edge is adjacent to the bottom one, and the left edge is adjacent to the right one.In line with Wilson-Cowan models (Wilson & Cowan, 1973), a single pair of e-and i-cell models the average activity of a local population of pyramidal neurons and underlying inhibitory interneurons within one cortical column (grey matter under approximately 0.25 square mm of the cortical surface).Cells are modelled as graded-response neurons (see below).
Each e-cell is restricted to send projections to the 19x19 e-cell neighbourhood within the same area, to topographically corresponding 19x19 e-cell patches in connected areas, and to a 5x5 i-cell patch in the inhibitory layer of the same area (Fig. 2).The probability of a synapse to be created between an e-cell and another cell falls off with their distance (Braitenberg & Schüz, 1998) according to a Gaussian function clipped to 0 outside the relevant neighbourhood.This produces a sparse, patchy and topographic connectivity, as typically found in the mammalian cortex (Amir et al., 1993;Kaas, 1997).

Membrane dynamics
The state of an (excitatory or inhibitory) cell at time is uniquely defined by its   membrane potential , determined by the following equation: where is the sum of all postsynaptic inputs acting upon cell (see Eq. ( 2)),   (, )  is a white noise process with uniform distribution over [-0.5, 0.5],  is the η ,  ( ) cell's membrane time constant (note that e-and i-cells have different , see Table 1), and and are scaling constants.Note that the activity of each e-cell is  1  2 intrinsically noisy, simulating the spontaneous baseline firing of real neurons (i-cells have =0).The total input to a cell is defined as: where E/IPSPs is the sum of all excitatory and inhibitory postsynaptic potentials -I/EPSPs; inhibitory synapses are given a negative sign -acting upon neural cluster (cell) at time , is the global (or area-specific) inhibition (see Eq. ( 3)) and k G is a scaling constant.Note that each e-cell gets exactly one IPSP from its twin i-cell (see Fig. 2).
The global inhibition mechanism is an area-specific inhibitory loop that prevents where is the sum of all e-cell outputs within area (see Eq. ( 4)) and τ G is ∈ ∑ (, )  the global inhibitory response time constant.
All cells produce a graded response representing the average firing rate of the neural cluster; in particular, the output (transformation function) of an e-cell e at time t is defined as: (4) Eq. ( 4) above is a piecewise-linear sigmoid function of the e-cell's membrane potential , clipped into the range [0, 1] and with slope 1 between the lower and (, ) upper thresholds and + 1.The output of an i-cell is 0 if (, ) (, ) (, )  (, ) < 0, and otherwise (i.e., unlike e-cells, i-cells do not saturate reflecting that (, ) real interneurons show little firing rate adaptation).

The threshold
of an e-cell is not constant but depends on the cell's recent (, ) activity, so that the more active the cell, the higher the threshold (see Eq. ( 5)).This implements a simple form of homeostatic adaptation (Matthews, 2001): (5) (, ) = α ω(, ) where is the estimated time-average of cell e's recent output (see Eq. ( 6)) and ω(, ) α is a scaling constant (adaptation strength).The estimated time-average of a ω(, ) cell's output is computed by integrating the following differential equation (Eq.( 6 Following Artola, Bröcher and Singer (Artola et al., 1990;Artola & Singer, 1993), the above weight-update rule -known as the "GWP" rule -accurately replicates well-documented synaptic plasticity phenomena of long-term potentiation (LTP) and depression (LTD), hence covering both Hebbian and "anti-Hebbian" phenomena.
Specifically, Eq. ( 6) implements voltage-dependent synaptic plasticity with one fixed pre-synaptic threshold θ pre (representing the minimum level of presynaptic activity required at a synapse for any weight change -LTP or LTD -to occur) and the current postsynaptic potential determining the "sign" of the change, based on two (, ) fixed post-synaptic thresholds θ -, θ + (see (Garagnani et al., 2009) for a discussion on the neurobiological realism of the GWP rule).